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Application of Genetic Algorithms and Data Mining in Noise-based Testing of Concurrent Software
Šimková, Hana ; Kofroň, Jan (oponent) ; Lourenco, Joao (oponent) ; Vojnar, Tomáš (vedoucí práce)
This thesis proposes an improvement of the efficiency of testing concurrent software by employing data mining techniques and genetic algorithms in the process of testing concurrent software. Concurrent, or multi-threaded, programming has become very popular over the last few years. However, as the concurrent programming is far more demanding the sequential programming, its increased use leads to a significant increase in the number of errors that appear in commercial software due to errors in synchronization. Finding such errors using traditional testing methods is difficult. Moreover, repeated test executions of traditional testing that are performed in the same environment will typically examine similar interleavings only. Hence, the noise-based injection approach is used for influencing the scheduling by injecting various kinds of noise (delays, context switches, and so on) into the common thread behaviour which stress the software and can to show some rare behaviour. However, for the noise injection to be efficient, one has to choose suitable noise injection heuristics from among the many existing ones as well as to suitably choose values of their various parameters, which is not easy. In this work, there are used data mining methods and genetic algorithms and their combinations to deal with the problem of choosing such noise injection heuristics and values of their parameters. Besides setting up of the goals of the thesis, this proposal also provides a brief summary of the state of the art in application of data mining techniques and genetic algorithms to program testing problems.
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Analysis and Testing of Concurrent Programs
Letko, Zdeněk ; Lourenco, Joao (oponent) ; Sekanina, Lukáš (oponent) ; Vojnar, Tomáš (vedoucí práce)
The thesis starts by providing a taxonomy of concurrency-related errors and an overview of their dynamic detection. Then, concurrency coverage metrics which measure how well the synchronisation and concurrency-related behaviour of tested programs has been examined are proposed together with a~methodology for deriving such metrics. The proposed metrics are especially suitable for saturation-based and search-based testing. Next, a novel coverage-based noise injection techniques that maximise the number of interleavings witnessed during testing are proposed. A comparison of various existing noise injection heuristics and the newly proposed heuristics on a set of benchmarks is provided, showing that the proposed techniques win over the existing ones in some cases. Finally, a novel use of stochastic optimisation algorithms in the area of concurrency testing is proposed in the form of their application for finding suitable combinations of values of the many parameters of tests and the noise injection techniques. The approach has been implemented in a prototype way and tested on a set of benchmark programs, showing its potential to significantly improve the testing process.
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Application of Genetic Algorithms and Data Mining in Noise-based Testing of Concurrent Software
Šimková, Hana ; Kofroň, Jan (oponent) ; Lourenco, Joao (oponent) ; Vojnar, Tomáš (vedoucí práce)
This thesis proposes an improvement of the efficiency of testing concurrent software by employing data mining techniques and genetic algorithms in the process of testing concurrent software. Concurrent, or multi-threaded, programming has become very popular over the last few years. However, as the concurrent programming is far more demanding the sequential programming, its increased use leads to a significant increase in the number of errors that appear in commercial software due to errors in synchronization. Finding such errors using traditional testing methods is difficult. Moreover, repeated test executions of traditional testing that are performed in the same environment will typically examine similar interleavings only. Hence, the noise-based injection approach is used for influencing the scheduling by injecting various kinds of noise (delays, context switches, and so on) into the common thread behaviour which stress the software and can to show some rare behaviour. However, for the noise injection to be efficient, one has to choose suitable noise injection heuristics from among the many existing ones as well as to suitably choose values of their various parameters, which is not easy. In this work, there are used data mining methods and genetic algorithms and their combinations to deal with the problem of choosing such noise injection heuristics and values of their parameters. Besides setting up of the goals of the thesis, this proposal also provides a brief summary of the state of the art in application of data mining techniques and genetic algorithms to program testing problems.
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Analysis and Testing of Concurrent Programs
Letko, Zdeněk ; Lourenco, Joao (oponent) ; Sekanina, Lukáš (oponent) ; Vojnar, Tomáš (vedoucí práce)
The thesis starts by providing a taxonomy of concurrency-related errors and an overview of their dynamic detection. Then, concurrency coverage metrics which measure how well the synchronisation and concurrency-related behaviour of tested programs has been examined are proposed together with a~methodology for deriving such metrics. The proposed metrics are especially suitable for saturation-based and search-based testing. Next, a novel coverage-based noise injection techniques that maximise the number of interleavings witnessed during testing are proposed. A comparison of various existing noise injection heuristics and the newly proposed heuristics on a set of benchmarks is provided, showing that the proposed techniques win over the existing ones in some cases. Finally, a novel use of stochastic optimisation algorithms in the area of concurrency testing is proposed in the form of their application for finding suitable combinations of values of the many parameters of tests and the noise injection techniques. The approach has been implemented in a prototype way and tested on a set of benchmark programs, showing its potential to significantly improve the testing process.
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